Quadruped Capturability and Push Recovery via a Switched-Systems Characterization of Dynamic Balance
Not on the map. Why?
Abstract (arXiv)
This paper studies capturability and push recovery for quadrupedal locomotion. Despite the rich literature on capturability analysis and push recovery control for legged robots, existing tools are developed mainly for bipeds or humanoids. Distinct quadrupedal features such as point contacts and multiple swinging legs prevent direct application of these methods. To address this gap, we propose a switched systems model for quadruped dynamics, and instantiate the abstract viability concept for quadrupedal locomotion with a time-based gait. Capturability is characterized through a novel specification of dynamically balanced states that addresses the time-varying nature of quadrupedal locomotion and balance. A linear inverted pendulum (LIP) model is adopted to demonstrate the theory and show how the newly developed quadrupedal capturability can be used in motion planning for quadrupedal push recovery. We formulate and solve an explicit model predictive control (EMPC) problem whose optimal solution fully characterizes quadrupedal capturability with the LIP. Given this analysis, an optimization-based planning scheme is devised for determining footsteps and center of mass references during push recovery. To validate the effectiveness of the overall framework, we conduct numerous simulation and hardware experiments. Simulation results illustrate the necessity of considering dynamic balance for quadrupedal capturability, and the significant improvement in disturbance rejection with the proposed strategy. Experimental validations on a replica of the Mini Cheetah quadruped demonstrate an up to 100% improvement as compared with state-of-the-art.
Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). First posted on arXiv 28 Jan 2022.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 1.
4 papers.
- 2022Safe Reinforcement Learning for Legged Locomotioncited by 29
- 2024Rethinking Robustness Assessment: Adversarial Attacks on Learning-based Quadrupedal Locomotion Controllerscited by 10
- 2025GuideNav: User-Informed Development of a Vision-Only Robotic Navigation Assistant For Blind Travelerscited by 3
- 2024Learning Emergent Gaits with Decentralized Phase Oscillators: on the role of Observations, Rewards, and Feedbackcited by 1
- 2025GuideNav: User-Informed Development of a Vision-Only Robotic Navigation Assistant For Blind Travelers5 Dec 2025
- 2024Rethinking Robustness Assessment: Adversarial Attacks on Learning-based Quadrupedal Locomotion Controllers21 May 2024
- 2024Learning Emergent Gaits with Decentralized Phase Oscillators: on the role of Observations, Rewards, and Feedback13 Feb 2024
- 2022Safe Reinforcement Learning for Legged Locomotion5 Mar 2022